9i`dZddlZddlZddlZddlZddlmZmZdZde de fdZ d d e d e fd Zy) z A collection of utilities for ensuring that training can always occur. Heavily influenced by the [toma](https://github.com/BlackHC/toma) library. N)is_npu_availableis_xpu_availablect|ts t|}tt|D]}d||< t j t r tjj|Str tjj|Stjj|S)aN Releases memory from `objects` by setting them to `None` and calls `gc.collect()` and `torch.cuda.empty_cache()`. Returned objects should be reassigned to the same variables. Args: objects (`Iterable`): An iterable of objects Returns: A list of `None` objects to replace `objects` Example: ```python >>> import torch >>> from accelerate.utils import release_memory >>> a = torch.ones(1000, 1000).cuda() >>> b = torch.ones(1000, 1000).cuda() >>> a, b = release_memory(a, b) ``` N) isinstancelistrangelengccollectrtorchxpu empty_cachernpucuda)objectsis a/var/www/html/backtest/airagagent/rag_env/lib/python3.12/site-packages/accelerate/utils/memory.pyrelease_memoryrs, gt $w- 3w<  JJL  N    N   N exceptionreturncgd}ttr,tjdk(rt fd|DSy)z Checks if `exception` relates to CUDA out-of-memory, CUDNN not supported, or CPU out-of-memory Args: exception (`Exception`): An exception )zCUDA out of memory.z(cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.z*DefaultCPUAllocator: can't allocate memoryrc3@K|]}|jdvyw)rN)args).0errrs r z+should_reduce_batch_size..OsC3)..++CsF)r RuntimeErrorr rany)r _statementss` rshould_reduce_batch_sizer"As:K )\*s9>>/Ba/GC{CCC rfunctionstarting_batch_sizecTtjt|S|fd}|S)a A basic decorator that will try to execute `function`. If it fails from exceptions related to out-of-memory or CUDNN, the batch size is cut in half and passed to `function` `function` must take in a `batch_size` parameter as its first argument. Args: function (`callable`, *optional*): A function to wrap starting_batch_size (`int`, *optional*): The batch size to try and fit into memory Example: ```python >>> from accelerate.utils import find_executable_batch_size >>> @find_executable_batch_size(starting_batch_size=128) ... def train(batch_size, model, optimizer): ... ... >>> train(model, optimizer) ``` )r$c tjtrtjj nGt rtjj ntjj ttjjj}t|t|dzkrbdjt!|dd|ddDcgc] \}}|d|c}}}t#dj$dj$d|d dk(r t'd  g|i|Scc}}w#t($r}t+|rtjtrtjj nGt rtjj ntjj d znYd}~nd}~wwxYw) Nrz, =zBatch size was passed into `zS` as the first argument when called.Remove this as the decorator already does so: `(z)`rz-No executable batch size found, reached zero.)r r rr rrrrrrinspect signature parameterskeysr joinzip TypeError__name__r Exceptionr") rkwargsparamsargvaluearg_stre batch_sizer#s r decoratorz-find_executable_batch_size..decoratorss   II ! ! #   II ! ! # JJ " " $g''1<<AACD v;#d)a- (iiCqr TXYZY[T\D] ^jc5C5%!1 ^_G.x/@/@.ABBBJBSBSATTUV]U^^`b Q"#RSS  rEsJ   7!H d$CCsCr